Overview

Dataset statistics

Number of variables41
Number of observations1176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory224.1 KiB
Average record size in memory195.1 B

Variable types

Numeric14
Categorical27

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
TotalWorkingYears is highly overall correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatus_SingleHigh correlation
BusinessTravel_Travel_Frequently is highly overall correlated with BusinessTravel_Travel_RarelyHigh correlation
BusinessTravel_Travel_Rarely is highly overall correlated with BusinessTravel_Travel_FrequentlyHigh correlation
EducationField_Life Sciences is highly overall correlated with EducationField_MedicalHigh correlation
EducationField_Medical is highly overall correlated with EducationField_Life SciencesHigh correlation
JobRole_Manager is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
JobRole_Research Director is highly overall correlated with MonthlyIncomeHigh correlation
MaritalStatus_Married is highly overall correlated with MaritalStatus_SingleHigh correlation
MaritalStatus_Single is highly overall correlated with StockOptionLevel and 1 other fieldsHigh correlation
EducationField_Other is highly imbalanced (70.2%)Imbalance
EducationField_Technical Degree is highly imbalanced (53.8%)Imbalance
JobRole_Human Resources is highly imbalanced (78.2%)Imbalance
JobRole_Manager is highly imbalanced (63.2%)Imbalance
JobRole_Manufacturing Director is highly imbalanced (54.4%)Imbalance
JobRole_Research Director is highly imbalanced (69.9%)Imbalance
JobRole_Sales Representative is highly imbalanced (70.9%)Imbalance

Reproduction

Analysis started2023-05-21 13:39:42.320281
Analysis finished2023-05-21 13:40:09.062943
Duration26.74 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.8909793 × 10-17
Minimum-2.0708311
Maximum2.5072053
Zeros0
Zeros (%)0.0%
Negative637
Negative (%)54.2%
Memory size9.3 KiB
2023-05-21T15:40:09.168225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.0708311
5-th percentile-1.4168259
Q1-0.76282068
median-0.10881549
Q30.65419056
95-th percentile1.8532001
Maximum2.5072053
Range4.5780363
Interquartile range (IQR)1.4170112

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.6982328 × 1016
Kurtosis-0.42959798
Mean-5.8909793 × 10-17
Median Absolute Deviation (MAD)0.65400519
Skewness0.40368726
Sum-4.4408921 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:09.313551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0.3268172188 64
 
5.4%
-0.217816354 57
 
4.8%
-0.1088154892 56
 
4.8%
-0.8718215428 56
 
4.8%
-0.6538198132 53
 
4.5%
-0.762820678 51
 
4.3%
-0.5448189484 51
 
4.3%
0.1091862404 48
 
4.1%
0.32718797 47
 
4.0%
-0.4358180836 47
 
4.0%
Other values (33) 646
54.9%
ValueCountFrequency (%)
-2.070831056 6
 
0.5%
-1.961830191 8
 
0.7%
-1.852829326 11
 
0.9%
-1.743828461 9
 
0.8%
-1.634827596 12
 
1.0%
-1.525826732 12
 
1.0%
-1.416825867 19
1.6%
-1.307825002 19
1.6%
-1.198824137 29
2.5%
-1.089823272 39
3.3%
ValueCountFrequency (%)
2.507205266 5
 
0.4%
2.398204401 7
 
0.6%
2.289203536 11
0.9%
2.180202672 4
 
0.3%
2.071201807 9
0.8%
1.962200942 20
1.7%
1.853200077 16
1.4%
1.744199212 16
1.4%
1.635198348 15
1.3%
1.526197483 12
1.0%

DailyRate
Real number (ℝ)

Distinct787
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3292466 × 10-16
Minimum-1.7473731
Maximum1.7324592
Zeros0
Zeros (%)0.0%
Negative591
Negative (%)50.3%
Memory size9.3 KiB
2023-05-21T15:40:09.439304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.7473731
5-th percentile-1.5859697
Q1-0.83815476
median-0.011196028
Q30.87995016
95-th percentile1.5492446
Maximum1.7324592
Range3.4798324
Interquartile range (IQR)1.7181049

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)7.5262591 × 1015
Kurtosis-1.1921753
Mean1.3292466 × 10-16
Median Absolute Deviation (MAD)0.85250908
Skewness-0.0040435727
Sum1.2664869 × 10-13
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:09.564925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9870945758 5
 
0.4%
-0.2816557789 4
 
0.3%
1.30869741 4
 
0.3%
-1.131672138 4
 
0.3%
-1.463203446 4
 
0.3%
-0.5359128717 4
 
0.3%
-1.712475105 4
 
0.3%
-0.682983151 4
 
0.3%
-1.184019187 4
 
0.3%
-0.8450097298 4
 
0.3%
Other values (777) 1135
96.5%
ValueCountFrequency (%)
-1.747373138 1
 
0.1%
-1.744880421 1
 
0.1%
-1.742387705 1
 
0.1%
-1.737402271 1
 
0.1%
-1.727431405 2
0.2%
-1.717460539 1
 
0.1%
-1.714967822 2
0.2%
-1.712475105 4
0.3%
-1.709982389 2
0.2%
-1.707489672 1
 
0.1%
ValueCountFrequency (%)
1.732459231 1
 
0.1%
1.729966515 1
 
0.1%
1.724981082 2
0.2%
1.722488365 2
0.2%
1.715010215 1
 
0.1%
1.710024782 3
0.3%
1.705039349 1
 
0.1%
1.697561199 3
0.3%
1.690083049 1
 
0.1%
1.685097616 1
 
0.1%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.0420301 × 10-17
Minimum-1.0222189
Maximum2.4023027
Zeros0
Zeros (%)0.0%
Negative746
Negative (%)63.4%
Memory size9.3 KiB
2023-05-21T15:40:09.687657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0222189
5-th percentile-1.0222189
Q1-0.89991452
median-0.28839281
Q30.56773759
95-th percentile2.0353897
Maximum2.4023027
Range3.4245216
Interquartile range (IQR)1.4676521

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.655777 × 1016
Kurtosis-0.29719222
Mean-6.0420301 × 10-17
Median Absolute Deviation (MAD)0.61152171
Skewness0.9282202
Sum-5.6843419 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:09.795221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-1.022218865 168
14.3%
-0.8999145224 163
13.9%
-0.2883928084 73
 
6.2%
0.07852022009 68
 
5.8%
-0.04378412273 67
 
5.7%
-0.1660884655 66
 
5.6%
-0.7776101796 58
 
4.9%
-0.533001494 56
 
4.8%
-0.6553058368 48
 
4.1%
-0.4106971512 47
 
4.0%
Other values (19) 362
30.8%
ValueCountFrequency (%)
-1.022218865 168
14.3%
-0.8999145224 163
13.9%
-0.7776101796 58
 
4.9%
-0.6553058368 48
 
4.1%
-0.533001494 56
 
4.8%
-0.4106971512 47
 
4.0%
-0.2883928084 73
6.2%
-0.1660884655 66
 
5.6%
-0.04378412273 67
 
5.7%
0.07852022009 68
5.8%
ValueCountFrequency (%)
2.402302734 22
1.9%
2.279998391 20
1.7%
2.157694048 10
0.9%
2.035389705 22
1.9%
1.913085362 20
1.7%
1.79078102 24
2.0%
1.668476677 21
1.8%
1.546172334 17
1.4%
1.423867991 15
1.3%
1.301563648 19
1.6%

Education
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.09102879217575739
455 
1.064209333982036
320 
-0.8821517496305211
224 
-1.8553322914367996
140 
2.0373898757883144
 
37

Length

Max length19
Median length19
Mean length18.42432
Min length17

Characters and Unicode

Total characters21667
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.064209333982036
2nd row-1.8553322914367996
3rd row-1.8553322914367996
4th row-1.8553322914367996
5th row2.0373898757883144

Common Values

ValueCountFrequency (%)
0.09102879217575739 455
38.7%
1.064209333982036 320
27.2%
-0.8821517496305211 224
19.0%
-1.8553322914367996 140
 
11.9%
2.0373898757883144 37
 
3.1%

Length

2023-05-21T15:40:09.916508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:10.050116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.09102879217575739 455
38.7%
1.064209333982036 320
27.2%
0.8821517496305211 224
19.0%
1.8553322914367996 140
 
11.9%
2.0373898757883144 37
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 2810
13.0%
9 2686
12.4%
3 2490
11.5%
1 2443
11.3%
2 2315
10.7%
7 2295
10.6%
5 1675
7.7%
8 1511
7.0%
. 1176
5.4%
6 1144
5.3%
Other values (2) 1122
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20127
92.9%
Other Punctuation 1176
 
5.4%
Dash Punctuation 364
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2810
14.0%
9 2686
13.3%
3 2490
12.4%
1 2443
12.1%
2 2315
11.5%
7 2295
11.4%
5 1675
8.3%
8 1511
7.5%
6 1144
5.7%
4 758
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21667
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2810
13.0%
9 2686
12.4%
3 2490
11.5%
1 2443
11.3%
2 2315
10.7%
7 2295
10.6%
5 1675
7.7%
8 1511
7.0%
. 1176
5.4%
6 1144
5.3%
Other values (2) 1122
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2810
13.0%
9 2686
12.4%
3 2490
11.5%
1 2443
11.3%
2 2315
10.7%
7 2295
10.6%
5 1675
7.7%
8 1511
7.0%
. 1176
5.4%
6 1144
5.3%
Other values (2) 1122
 
5.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.2602020720875338
376 
1.1791138942345
348 
-1.5776215722063986
229 
-0.6587097500594324
223 

Length

Max length19
Median length18
Mean length17.496599
Min length15

Characters and Unicode

Total characters20576
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.6587097500594324
2nd row0.2602020720875338
3rd row-1.5776215722063986
4th row-0.6587097500594324
5th row1.1791138942345

Common Values

ValueCountFrequency (%)
0.2602020720875338 376
32.0%
1.1791138942345 348
29.6%
-1.5776215722063986 229
19.5%
-0.6587097500594324 223
19.0%

Length

2023-05-21T15:40:10.168905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:10.285968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.2602020720875338 376
32.0%
1.1791138942345 348
29.6%
1.5776215722063986 229
19.5%
0.6587097500594324 223
19.0%

Most occurring characters

ValueCountFrequency (%)
0 3001
14.6%
2 2762
13.4%
7 2233
10.9%
3 1900
9.2%
5 1851
9.0%
1 1850
9.0%
8 1552
7.5%
9 1371
6.7%
6 1286
6.2%
. 1176
 
5.7%
Other values (2) 1594
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18948
92.1%
Other Punctuation 1176
 
5.7%
Dash Punctuation 452
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3001
15.8%
2 2762
14.6%
7 2233
11.8%
3 1900
10.0%
5 1851
9.8%
1 1850
9.8%
8 1552
8.2%
9 1371
7.2%
6 1286
6.8%
4 1142
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3001
14.6%
2 2762
13.4%
7 2233
10.9%
3 1900
9.2%
5 1851
9.0%
1 1850
9.0%
8 1552
7.5%
9 1371
6.7%
6 1286
6.2%
. 1176
 
5.7%
Other values (2) 1594
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3001
14.6%
2 2762
13.4%
7 2233
10.9%
3 1900
9.2%
5 1851
9.0%
1 1850
9.0%
8 1552
7.5%
9 1371
6.7%
6 1286
6.2%
. 1176
 
5.7%
Other values (2) 1594
7.7%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.37356374698936007
705 
-1.0481545910672325
294 
1.7952820850459525
113 
-2.469872929123825
 
64

Length

Max length19
Median length19
Mean length18.84949
Min length18

Characters and Unicode

Total characters22167
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.7952820850459525
2nd row0.37356374698936007
3rd row0.37356374698936007
4th row0.37356374698936007
5th row0.37356374698936007

Common Values

ValueCountFrequency (%)
0.37356374698936007 705
59.9%
-1.0481545910672325 294
25.0%
1.7952820850459525 113
 
9.6%
-2.469872929123825 64
 
5.4%

Length

2023-05-21T15:40:10.388878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:10.499903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.37356374698936007 705
59.9%
1.0481545910672325 294
25.0%
1.7952820850459525 113
 
9.6%
2.469872929123825 64
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 3178
14.3%
0 2929
13.2%
7 2586
11.7%
6 2473
11.2%
5 2216
10.0%
9 2122
9.6%
4 1470
6.6%
8 1353
6.1%
2 1247
 
5.6%
. 1176
 
5.3%
Other values (2) 1417
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20633
93.1%
Other Punctuation 1176
 
5.3%
Dash Punctuation 358
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3178
15.4%
0 2929
14.2%
7 2586
12.5%
6 2473
12.0%
5 2216
10.7%
9 2122
10.3%
4 1470
7.1%
8 1353
6.6%
2 1247
 
6.0%
1 1059
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3178
14.3%
0 2929
13.2%
7 2586
11.7%
6 2473
11.2%
5 2216
10.0%
9 2122
9.6%
4 1470
6.6%
8 1353
6.1%
2 1247
 
5.6%
. 1176
 
5.3%
Other values (2) 1417
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3178
14.3%
0 2929
13.2%
7 2586
11.7%
6 2473
11.2%
5 2216
10.0%
9 2122
9.6%
4 1470
6.6%
8 1353
6.1%
2 1247
 
5.6%
. 1176
 
5.3%
Other values (2) 1417
6.4%

JobLevel
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
-0.0701136189928391
436 
-0.9862649071659377
420 
0.8460376691802596
179 
1.7621889573533582
92 
2.678340245526457
49 

Length

Max length19
Median length19
Mean length18.686224
Min length17

Characters and Unicode

Total characters21975
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.7621889573533582
2nd row-0.9862649071659377
3rd row1.7621889573533582
4th row-0.9862649071659377
5th row-0.0701136189928391

Common Values

ValueCountFrequency (%)
-0.0701136189928391 436
37.1%
-0.9862649071659377 420
35.7%
0.8460376691802596 179
15.2%
1.7621889573533582 92
 
7.8%
2.678340245526457 49
 
4.2%

Length

2023-05-21T15:40:10.608398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:10.732105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0701136189928391 436
37.1%
0.9862649071659377 420
35.7%
0.8460376691802596 179
15.2%
1.7621889573533582 92
 
7.8%
2.678340245526457 49
 
4.2%

Most occurring characters

ValueCountFrequency (%)
9 3018
13.7%
0 2734
12.4%
6 2602
11.8%
1 2527
11.5%
7 2157
9.8%
8 1975
9.0%
3 1796
8.2%
2 1366
6.2%
. 1176
 
5.4%
5 1022
 
4.7%
Other values (2) 1602
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19943
90.8%
Other Punctuation 1176
 
5.4%
Dash Punctuation 856
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 3018
15.1%
0 2734
13.7%
6 2602
13.0%
1 2527
12.7%
7 2157
10.8%
8 1975
9.9%
3 1796
9.0%
2 1366
6.8%
5 1022
 
5.1%
4 746
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 3018
13.7%
0 2734
12.4%
6 2602
11.8%
1 2527
11.5%
7 2157
9.8%
8 1975
9.0%
3 1796
8.2%
2 1366
6.2%
. 1176
 
5.4%
5 1022
 
4.7%
Other values (2) 1602
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 3018
13.7%
0 2734
12.4%
6 2602
11.8%
1 2527
11.5%
7 2157
9.8%
8 1975
9.0%
3 1796
8.2%
2 1366
6.2%
. 1176
 
5.4%
5 1022
 
4.7%
Other values (2) 1602
7.3%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1.1535262668585071
366 
0.25276471982955345
354 
-1.5487583742283537
240 
-0.6479968271994002
216 

Length

Max length19
Median length19
Mean length18.688776
Min length18

Characters and Unicode

Total characters21978
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.6479968271994002
2nd row1.1535262668585071
3rd row0.25276471982955345
4th row0.25276471982955345
5th row0.25276471982955345

Common Values

ValueCountFrequency (%)
1.1535262668585071 366
31.1%
0.25276471982955345 354
30.1%
-1.5487583742283537 240
20.4%
-0.6479968271994002 216
18.4%

Length

2023-05-21T15:40:10.838819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:10.946532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.1535262668585071 366
31.1%
0.25276471982955345 354
30.1%
1.5487583742283537 240
20.4%
0.6479968271994002 216
18.4%

Most occurring characters

ValueCountFrequency (%)
5 3600
16.4%
2 2706
12.3%
7 2226
10.1%
8 2022
9.2%
1 1908
8.7%
6 1884
8.6%
4 1620
7.4%
9 1572
7.2%
3 1440
 
6.6%
0 1368
 
6.2%
Other values (2) 1632
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20346
92.6%
Other Punctuation 1176
 
5.4%
Dash Punctuation 456
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3600
17.7%
2 2706
13.3%
7 2226
10.9%
8 2022
9.9%
1 1908
9.4%
6 1884
9.3%
4 1620
8.0%
9 1572
7.7%
3 1440
 
7.1%
0 1368
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21978
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3600
16.4%
2 2706
12.3%
7 2226
10.1%
8 2022
9.2%
1 1908
8.7%
6 1884
8.6%
4 1620
7.4%
9 1572
7.2%
3 1440
 
6.6%
0 1368
 
6.2%
Other values (2) 1632
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3600
16.4%
2 2706
12.3%
7 2226
10.1%
8 2022
9.2%
1 1908
8.7%
6 1884
8.6%
4 1620
7.4%
9 1572
7.2%
3 1440
 
6.6%
0 1368
 
6.2%
Other values (2) 1632
7.4%

MonthlyIncome
Real number (ℝ)

Distinct1098
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.2504361 × 10-17
Minimum-1.1898763
Maximum2.886856
Zeros0
Zeros (%)0.0%
Negative774
Negative (%)65.8%
Memory size9.3 KiB
2023-05-21T15:40:11.070913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1898763
5-th percentile-0.95200956
Q1-0.77304523
median-0.33095496
Q30.40338998
95-th percentile2.3531876
Maximum2.886856
Range4.0767323
Interquartile range (IQR)1.1764352

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.3798142 × 1016
Kurtosis0.92103034
Mean-7.2504361 × 10-17
Median Absolute Deviation (MAD)0.49174884
Skewness1.3329503
Sum-9.1482377 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:11.205209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9033183638 4
 
0.3%
-0.8175444676 3
 
0.3%
-0.8798863972 3
 
0.3%
-0.845705822 3
 
0.3%
-0.664699254 3
 
0.3%
-0.2111079731 3
 
0.3%
-0.7713254508 2
 
0.2%
-0.04235481888 2
 
0.2%
-0.7756248943 2
 
0.2%
-0.08212467052 2
 
0.2%
Other values (1088) 1149
97.7%
ValueCountFrequency (%)
-1.189876268 1
0.1%
-1.180847437 1
0.1%
-1.174398272 1
0.1%
-1.17224855 1
0.1%
-1.169883856 1
0.1%
-1.143872223 1
0.1%
-1.141937474 1
0.1%
-1.135703281 1
0.1%
-1.132908643 1
0.1%
-1.131403837 1
0.1%
ValueCountFrequency (%)
2.886855984 1
0.1%
2.880406818 1
0.1%
2.876752292 1
0.1%
2.862349156 1
0.1%
2.85976949 1
0.1%
2.859339546 1
0.1%
2.85675988 1
0.1%
2.820644555 1
0.1%
2.81913975 1
0.1%
2.814410362 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.2504361 × 10-17
Minimum-1.0837043
Maximum2.5379966
Zeros0
Zeros (%)0.0%
Negative686
Negative (%)58.3%
Memory size9.3 KiB
2023-05-21T15:40:11.320623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0837043
5-th percentile-1.0837043
Q1-0.68129313
median-0.27888192
Q30.5259405
95-th percentile2.1355853
Maximum2.5379966
Range3.6217009
Interquartile range (IQR)1.2072336

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.3798142 × 1016
Kurtosis-0.052565136
Mean-7.2504361 × 10-17
Median Absolute Deviation (MAD)0.40241121
Skewness0.99907072
Sum-5.6843419 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:11.408484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.6812931309 415
35.3%
-1.083704342 160
 
13.6%
0.1235292919 126
 
10.7%
0.5259405033 118
 
10.0%
-0.2788819195 111
 
9.4%
1.733174138 66
 
5.6%
0.9283517147 52
 
4.4%
1.330762926 51
 
4.3%
2.135585349 41
 
3.5%
2.53799656 36
 
3.1%
ValueCountFrequency (%)
-1.083704342 160
 
13.6%
-0.6812931309 415
35.3%
-0.2788819195 111
 
9.4%
0.1235292919 126
 
10.7%
0.5259405033 118
 
10.0%
0.9283517147 52
 
4.4%
1.330762926 51
 
4.3%
1.733174138 66
 
5.6%
2.135585349 41
 
3.5%
2.53799656 36
 
3.1%
ValueCountFrequency (%)
2.53799656 36
 
3.1%
2.135585349 41
 
3.5%
1.733174138 66
 
5.6%
1.330762926 51
 
4.3%
0.9283517147 52
 
4.4%
0.5259405033 118
 
10.0%
0.1235292919 126
 
10.7%
-0.2788819195 111
 
9.4%
-0.6812931309 415
35.3%
-1.083704342 160
 
13.6%

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6313481 × 10-16
Minimum-1.1528964
Maximum2.6540202
Zeros0
Zeros (%)0.0%
Negative730
Negative (%)62.1%
Memory size9.3 KiB
2023-05-21T15:40:11.509015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1528964
5-th percentile-1.1528964
Q1-0.88097377
median-0.33712854
Q30.7505619
95-th percentile1.8382523
Maximum2.6540202
Range3.8069166
Interquartile range (IQR)1.6315357

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-6.1325074 × 1015
Kurtosis-0.33061221
Mean-1.6313481 × 10-16
Median Absolute Deviation (MAD)0.54384522
Skewness0.80666244
Sum-1.9184654 × 10-13
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:11.604757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-1.152896378 168
14.3%
-0.6090511553 167
14.2%
-0.8809737668 158
13.4%
-0.3371285438 156
13.3%
-0.06520593235 81
6.9%
0.7505619021 72
6.1%
0.4786392906 64
 
5.4%
0.2067166791 63
 
5.4%
1.022484514 62
 
5.3%
1.294407125 49
 
4.2%
Other values (5) 136
11.6%
ValueCountFrequency (%)
-1.152896378 168
14.3%
-0.8809737668 158
13.4%
-0.6090511553 167
14.2%
-0.3371285438 156
13.3%
-0.06520593235 81
6.9%
0.2067166791 63
 
5.4%
0.4786392906 64
 
5.4%
0.7505619021 72
6.1%
1.022484514 62
 
5.3%
1.294407125 49
 
4.2%
ValueCountFrequency (%)
2.654020183 16
 
1.4%
2.382097571 15
 
1.3%
2.11017496 25
 
2.1%
1.838252348 43
3.7%
1.566329737 37
3.1%
1.294407125 49
4.2%
1.022484514 62
5.3%
0.7505619021 72
6.1%
0.4786392906 64
5.4%
0.2067166791 63
5.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1.1604029781681362
365 
0.24021828341039647
355 
-0.6799664113473433
240 
-1.600151106105083
216 

Length

Max length19
Median length19
Mean length18.505952
Min length18

Characters and Unicode

Total characters21763
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.24021828341039647
2nd row0.24021828341039647
3rd row1.1604029781681362
4th row0.24021828341039647
5th row-0.6799664113473433

Common Values

ValueCountFrequency (%)
1.1604029781681362 365
31.0%
0.24021828341039647 355
30.2%
-0.6799664113473433 240
20.4%
-1.600151106105083 216
18.4%

Length

2023-05-21T15:40:11.716718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:11.829768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.1604029781681362 365
31.0%
0.24021828341039647 355
30.2%
0.6799664113473433 240
20.4%
1.600151106105083 216
18.4%

Most occurring characters

ValueCountFrequency (%)
1 3730
17.1%
0 3115
14.3%
6 2602
12.0%
3 2251
10.3%
4 2150
9.9%
2 1795
8.2%
8 1656
7.6%
9 1200
 
5.5%
7 1200
 
5.5%
. 1176
 
5.4%
Other values (2) 888
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20131
92.5%
Other Punctuation 1176
 
5.4%
Dash Punctuation 456
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3730
18.5%
0 3115
15.5%
6 2602
12.9%
3 2251
11.2%
4 2150
10.7%
2 1795
8.9%
8 1656
8.2%
9 1200
 
6.0%
7 1200
 
6.0%
5 432
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3730
17.1%
0 3115
14.3%
6 2602
12.0%
3 2251
10.3%
4 2150
9.9%
2 1795
8.2%
8 1656
7.6%
9 1200
 
5.5%
7 1200
 
5.5%
. 1176
 
5.4%
Other values (2) 888
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3730
17.1%
0 3115
14.3%
6 2602
12.0%
3 2251
10.3%
4 2150
9.9%
2 1795
8.2%
8 1656
7.6%
9 1200
 
5.5%
7 1200
 
5.5%
. 1176
 
5.4%
Other values (2) 888
 
4.1%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
-0.9354054242077128
503 
0.2474298218872014
482 
1.4302650679821156
125 
2.61310031407703
66 

Length

Max length19
Median length18
Mean length18.315476
Min length16

Characters and Unicode

Total characters21539
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.61310031407703
2nd row0.2474298218872014
3rd row0.2474298218872014
4th row-0.9354054242077128
5th row0.2474298218872014

Common Values

ValueCountFrequency (%)
-0.9354054242077128 503
42.8%
0.2474298218872014 482
41.0%
1.4302650679821156 125
 
10.6%
2.61310031407703 66
 
5.6%

Length

2023-05-21T15:40:11.936518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:12.049410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.9354054242077128 503
42.8%
0.2474298218872014 482
41.0%
1.4302650679821156 125
 
10.6%
2.61310031407703 66
 
5.6%

Most occurring characters

ValueCountFrequency (%)
2 3753
17.4%
4 3146
14.6%
0 2987
13.9%
7 2227
10.3%
8 2074
9.6%
1 2040
9.5%
5 1256
 
5.8%
. 1176
 
5.5%
9 1110
 
5.2%
3 826
 
3.8%
Other values (2) 944
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19860
92.2%
Other Punctuation 1176
 
5.5%
Dash Punctuation 503
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3753
18.9%
4 3146
15.8%
0 2987
15.0%
7 2227
11.2%
8 2074
10.4%
1 2040
10.3%
5 1256
 
6.3%
9 1110
 
5.6%
3 826
 
4.2%
6 441
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3753
17.4%
4 3146
14.6%
0 2987
13.9%
7 2227
10.3%
8 2074
9.6%
1 2040
9.5%
5 1256
 
5.8%
. 1176
 
5.5%
9 1110
 
5.2%
3 826
 
3.8%
Other values (2) 944
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3753
17.4%
4 3146
14.6%
0 2987
13.9%
7 2227
10.3%
8 2074
9.6%
1 2040
9.5%
5 1256
 
5.8%
. 1176
 
5.5%
9 1110
 
5.2%
3 826
 
3.8%
Other values (2) 944
 
4.4%

TotalWorkingYears
Real number (ℝ)

Distinct40
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.2504361 × 10-17
Minimum-1.4573851
Maximum3.6720869
Zeros0
Zeros (%)0.0%
Negative764
Negative (%)65.0%
Memory size9.3 KiB
2023-05-21T15:40:12.157121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.4573851
5-th percentile-1.3291483
Q1-0.68796426
median-0.17501706
Q30.46616694
95-th percentile2.1332453
Maximum3.6720869
Range5.129472
Interquartile range (IQR)1.1541312

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.3798142 × 1016
Kurtosis0.86267664
Mean-7.2504361 × 10-17
Median Absolute Deviation (MAD)0.5129472
Skewness1.1037068
Sum-8.4821039 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:12.385973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-0.1750170615 160
 
13.6%
-0.6879642621 99
 
8.4%
-0.4314906618 87
 
7.4%
-0.3032538616 79
 
6.7%
-0.8162010623 76
 
6.5%
-1.329148263 63
 
5.4%
-0.559727462 52
 
4.4%
-0.9444378625 49
 
4.2%
0.08145653888 38
 
3.2%
-0.04678026129 34
 
2.9%
Other values (30) 439
37.3%
ValueCountFrequency (%)
-1.457385063 9
 
0.8%
-1.329148263 63
5.4%
-1.200911463 24
 
2.0%
-1.072674663 32
 
2.7%
-0.9444378625 49
4.2%
-0.8162010623 76
6.5%
-0.6879642621 99
8.4%
-0.559727462 52
4.4%
-0.4314906618 87
7.4%
-0.3032538616 79
6.7%
ValueCountFrequency (%)
3.672086944 1
 
0.1%
3.415613343 1
 
0.1%
3.287376543 4
0.3%
3.159139743 5
0.4%
3.030902943 3
 
0.3%
2.902666143 2
 
0.2%
2.774429342 6
0.5%
2.646192542 6
0.5%
2.517955742 9
0.8%
2.389718942 7
0.6%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.625218 × 10-17
Minimum-2.1980915
Maximum2.580015
Zeros0
Zeros (%)0.0%
Negative554
Negative (%)47.1%
Memory size9.3 KiB
2023-05-21T15:40:12.478397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.1980915
5-th percentile-1.4017404
Q1-0.60538935
median0.19096174
Q30.19096174
95-th percentile1.7836639
Maximum2.580015
Range4.7781066
Interquartile range (IQR)0.79635109

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)2.7596283 × 1016
Kurtosis0.70438706
Mean3.625218 × 10-17
Median Absolute Deviation (MAD)0.79635109
Skewness0.61412914
Sum4.2632564 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:12.560288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.6053893511 457
38.9%
0.1909617416 394
33.5%
0.9873128343 95
 
8.1%
1.783663927 84
 
7.1%
-1.401740444 56
 
4.8%
2.58001502 49
 
4.2%
-2.198091537 41
 
3.5%
ValueCountFrequency (%)
-2.198091537 41
 
3.5%
-1.401740444 56
 
4.8%
-0.6053893511 457
38.9%
0.1909617416 394
33.5%
0.9873128343 95
 
8.1%
1.783663927 84
 
7.1%
2.58001502 49
 
4.2%
ValueCountFrequency (%)
2.58001502 49
 
4.2%
1.783663927 84
 
7.1%
0.9873128343 95
 
8.1%
0.1909617416 394
33.5%
-0.6053893511 457
38.9%
-1.401740444 56
 
4.8%
-2.198091537 41
 
3.5%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.33762109125915835
711 
-1.0555101484628426
270 
1.7307523309811592
125 
-2.4486413881848437
 
70

Length

Max length19
Median length19
Mean length18.893707
Min length18

Characters and Unicode

Total characters22219
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.33762109125915835
2nd row0.33762109125915835
3rd row0.33762109125915835
4th row0.33762109125915835
5th row0.33762109125915835

Common Values

ValueCountFrequency (%)
0.33762109125915835 711
60.5%
-1.0555101484628426 270
 
23.0%
1.7307523309811592 125
 
10.6%
-2.4486413881848437 70
 
6.0%

Length

2023-05-21T15:40:12.649829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:12.759012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.33762109125915835 711
60.5%
1.0555101484628426 270
 
23.0%
1.7307523309811592 125
 
10.6%
2.4486413881848437 70
 
6.0%

Most occurring characters

ValueCountFrequency (%)
1 3458
15.6%
5 3193
14.4%
3 2648
11.9%
2 2282
10.3%
0 2212
10.0%
8 1726
7.8%
9 1672
7.5%
6 1321
 
5.9%
. 1176
 
5.3%
4 1160
 
5.2%
Other values (2) 1371
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20703
93.2%
Other Punctuation 1176
 
5.3%
Dash Punctuation 340
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3458
16.7%
5 3193
15.4%
3 2648
12.8%
2 2282
11.0%
0 2212
10.7%
8 1726
8.3%
9 1672
8.1%
6 1321
 
6.4%
4 1160
 
5.6%
7 1031
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3458
15.6%
5 3193
14.4%
3 2648
11.9%
2 2282
10.3%
0 2212
10.0%
8 1726
7.8%
9 1672
7.5%
6 1321
 
5.9%
. 1176
 
5.3%
4 1160
 
5.2%
Other values (2) 1371
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3458
15.6%
5 3193
14.4%
3 2648
11.9%
2 2282
10.3%
0 2212
10.0%
8 1726
7.8%
9 1672
7.5%
6 1321
 
5.9%
. 1176
 
5.3%
4 1160
 
5.2%
Other values (2) 1371
 
6.2%

YearsAtCompany
Real number (ℝ)

Distinct36
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9258971 × 10-17
Minimum-1.1588006
Maximum4.9227013
Zeros0
Zeros (%)0.0%
Negative753
Negative (%)64.0%
Memory size9.3 KiB
2023-05-21T15:40:12.866502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1588006
5-th percentile-0.99443571
Q1-0.66570587
median-0.33697604
Q30.48484856
95-th percentile2.1284977
Maximum4.9227013
Range6.081502
Interquartile range (IQR)1.1505544

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-5.1945945 × 1016
Kurtosis3.7103599
Mean-1.9258971 × 10-17
Median Absolute Deviation (MAD)0.49309475
Skewness1.7328975
Sum5.7731597 × 10-15
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:12.976269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
-0.3369760355 165
14.0%
-0.9944357083 132
11.2%
-0.6657058719 101
8.6%
0.4848485555 100
8.5%
-0.8300707901 100
8.5%
-0.5013409537 83
 
7.1%
-0.008246199127 78
 
6.6%
0.1561187191 66
 
5.6%
0.3204836373 60
 
5.1%
-0.1726111173 60
 
5.1%
Other values (26) 231
19.6%
ValueCountFrequency (%)
-1.158800626 34
 
2.9%
-0.9944357083 132
11.2%
-0.8300707901 100
8.5%
-0.6657058719 101
8.6%
-0.5013409537 83
7.1%
-0.3369760355 165
14.0%
-0.1726111173 60
 
5.1%
-0.008246199127 78
6.6%
0.1561187191 66
 
5.6%
0.3204836373 60
 
5.1%
ValueCountFrequency (%)
4.922701347 1
 
0.1%
4.758336429 2
0.2%
4.429606592 1
 
0.1%
4.265241674 3
0.3%
4.100876756 2
0.2%
3.936511838 3
0.3%
3.772146919 1
 
0.1%
3.607782001 2
0.2%
3.279052165 2
0.2%
3.114687247 4
0.3%

YearsInCurrentRole
Real number (ℝ)

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2657613 × 10-17
Minimum-1.1859053
Maximum3.5786886
Zeros0
Zeros (%)0.0%
Negative727
Negative (%)61.8%
Memory size9.3 KiB
2023-05-21T15:40:13.077994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1859053
5-th percentile-1.1859053
Q1-0.62536487
median-0.34509464
Q30.77598629
95-th percentile1.616797
Maximum3.5786886
Range4.7645939
Interquartile range (IQR)1.4013512

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)4.4154053 × 1016
Kurtosis0.36188762
Mean2.2657613 × 10-17
Median Absolute Deviation (MAD)0.8408107
Skewness0.87292763
Sum4.0856207 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:13.174074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-0.6253648713 295
25.1%
-1.185905335 189
16.1%
0.7759862885 185
15.7%
-0.3450946394 105
 
8.9%
-0.0648244074 88
 
7.5%
1.056256521 72
 
6.1%
1.336526752 54
 
4.6%
-0.9056351033 50
 
4.3%
0.2154458246 28
 
2.4%
0.4957160566 28
 
2.4%
Other values (8) 82
 
7.0%
ValueCountFrequency (%)
-1.185905335 189
16.1%
-0.9056351033 50
 
4.3%
-0.6253648713 295
25.1%
-0.3450946394 105
 
8.9%
-0.0648244074 88
 
7.5%
0.2154458246 28
 
2.4%
0.4957160566 28
 
2.4%
0.7759862885 185
15.7%
1.056256521 72
 
6.1%
1.336526752 54
 
4.6%
ValueCountFrequency (%)
3.578688608 3
 
0.3%
3.298418376 7
 
0.6%
3.018148144 6
 
0.5%
2.737877912 9
 
0.8%
2.45760768 8
 
0.7%
2.177337448 6
 
0.5%
1.897067216 19
 
1.6%
1.616796984 24
 
2.0%
1.336526752 54
4.6%
1.056256521 72
6.1%

YearsSinceLastPromotion
Real number (ℝ)

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.8336241 × 10-17
Minimum-0.67916501
Maximum3.9879448
Zeros0
Zeros (%)0.0%
Negative877
Negative (%)74.6%
Memory size9.3 KiB
2023-05-21T15:40:13.267256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.67916501
5-th percentile-0.67916501
Q1-0.67916501
median-0.36802436
Q30.25425694
95-th percentile2.1211009
Maximum3.9879448
Range4.6671098
Interquartile range (IQR)0.93342195

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-2.0697212 × 1016
Kurtosis3.5327187
Mean-4.8336241 × 10-17
Median Absolute Deviation (MAD)0.31114065
Skewness1.970864
Sum-1.1368684 × 10-13
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:13.357429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-0.6791650102 467
39.7%
-0.3680243588 286
24.3%
-0.05688370751 124
 
10.5%
1.498819549 62
 
5.3%
0.5653975951 52
 
4.4%
0.2542569438 40
 
3.4%
0.8765382465 35
 
3.0%
1.187678898 25
 
2.1%
2.743382154 20
 
1.7%
2.121100852 14
 
1.2%
Other values (6) 51
 
4.3%
ValueCountFrequency (%)
-0.6791650102 467
39.7%
-0.3680243588 286
24.3%
-0.05688370751 124
 
10.5%
0.2542569438 40
 
3.4%
0.5653975951 52
 
4.4%
0.8765382465 35
 
3.0%
1.187678898 25
 
2.1%
1.498819549 62
 
5.3%
1.8099602 14
 
1.2%
2.121100852 14
 
1.2%
ValueCountFrequency (%)
3.98794476 9
 
0.8%
3.676804108 7
 
0.6%
3.365663457 9
 
0.8%
3.054522806 9
 
0.8%
2.743382154 20
 
1.7%
2.432241503 3
 
0.3%
2.121100852 14
 
1.2%
1.8099602 14
 
1.2%
1.498819549 62
5.3%
1.187678898 25
2.1%

YearsWithCurrManager
Real number (ℝ)

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7217584 × 10-17
Minimum-1.1776872
Maximum3.5931989
Zeros0
Zeros (%)0.0%
Negative726
Negative (%)61.7%
Memory size9.3 KiB
2023-05-21T15:40:13.448153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1776872
5-th percentile-1.1776872
Q1-0.6164065
median-0.33576614
Q30.78679529
95-th percentile1.6988764
Maximum3.5931989
Range4.7708861
Interquartile range (IQR)1.4032018

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)1.4883389 × 1016
Kurtosis0.029167955
Mean6.7217584 × 10-17
Median Absolute Deviation (MAD)0.84192107
Skewness0.77803748
Sum7.9047879 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:13.543307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-0.6164064978 277
23.6%
-1.177687211 203
17.3%
0.7867952858 177
15.1%
-0.3357661411 109
 
9.3%
1.067435643 90
 
7.7%
-0.05512578436 79
 
6.7%
-0.8970468545 58
 
4.9%
1.348075999 52
 
4.4%
0.2255145724 25
 
2.1%
1.628716356 24
 
2.0%
Other values (8) 82
 
7.0%
ValueCountFrequency (%)
-1.177687211 203
17.3%
-0.8970468545 58
 
4.9%
-0.6164064978 277
23.6%
-0.3357661411 109
 
9.3%
-0.05512578436 79
 
6.7%
0.2255145724 25
 
2.1%
0.5061549291 23
 
2.0%
0.7867952858 177
15.1%
1.067435643 90
 
7.7%
1.348075999 52
 
4.4%
ValueCountFrequency (%)
3.593198853 5
 
0.4%
3.312558496 2
 
0.2%
3.03191814 3
 
0.3%
2.751277783 4
 
0.3%
2.470637426 10
 
0.9%
2.189997069 17
 
1.4%
1.909356713 18
 
1.5%
1.628716356 24
 
2.0%
1.348075999 52
4.4%
1.067435643 90
7.7%

AverageSatisfaction
Real number (ℝ)

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.4680372 × 10-16
Minimum-2.7371672
Maximum2.0229671
Zeros0
Zeros (%)0.0%
Negative655
Negative (%)55.7%
Memory size9.3 KiB
2023-05-21T15:40:13.630062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.7371672
5-th percentile-1.6793596
Q1-0.62155193
median-0.092648117
Q30.96515951
95-th percentile1.4940633
Maximum2.0229671
Range4.7601343
Interquartile range (IQR)1.5867114

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-1.8295878 × 1015
Kurtosis-0.40547242
Mean-5.4680372 × 10-16
Median Absolute Deviation (MAD)0.52890381
Skewness-0.14506052
Sum-6.4304118 × 10-13
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:13.714044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.09264811722 227
19.3%
0.4362556976 216
18.4%
-0.621551932 196
16.7%
0.9651595124 170
14.5%
-1.150455747 139
11.8%
1.494063327 99
8.4%
-1.679359562 66
 
5.6%
2.022967142 36
 
3.1%
-2.208263376 16
 
1.4%
-2.737167191 11
 
0.9%
ValueCountFrequency (%)
-2.737167191 11
 
0.9%
-2.208263376 16
 
1.4%
-1.679359562 66
 
5.6%
-1.150455747 139
11.8%
-0.621551932 196
16.7%
-0.09264811722 227
19.3%
0.4362556976 216
18.4%
0.9651595124 170
14.5%
1.494063327 99
8.4%
2.022967142 36
 
3.1%
ValueCountFrequency (%)
2.022967142 36
 
3.1%
1.494063327 99
8.4%
0.9651595124 170
14.5%
0.4362556976 216
18.4%
-0.09264811722 227
19.3%
-0.621551932 196
16.7%
-1.150455747 139
11.8%
-1.679359562 66
 
5.6%
-2.208263376 16
 
1.4%
-2.737167191 11
 
0.9%

SalaryDeviation
Real number (ℝ)

Distinct1109
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9636598 × 10-17
Minimum-3.4764153
Maximum3.3894218
Zeros0
Zeros (%)0.0%
Negative647
Negative (%)55.0%
Memory size9.3 KiB
2023-05-21T15:40:13.824537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3.4764153
5-th percentile-1.6426668
Q1-0.54067125
median-0.076506363
Q30.54999419
95-th percentile1.6423048
Maximum3.3894218
Range6.8658371
Interquartile range (IQR)1.0906654

Descriptive statistics

Standard deviation1.0004254
Coefficient of variation (CV)-5.0946985 × 1016
Kurtosis1.4366032
Mean-1.9636598 × 10-17
Median Absolute Deviation (MAD)0.55256812
Skewness0.39453793
Sum-1.2434498 × 10-14
Variance1.0008511
MonotonicityNot monotonic
2023-05-21T15:40:13.946933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3370100399 4
 
0.3%
-0.2547977711 3
 
0.3%
-0.1348734524 3
 
0.3%
0.04360872834 3
 
0.3%
0.5001974679 3
 
0.3%
-0.2178399622 2
 
0.2%
0.4810685072 2
 
0.2%
-0.4572072943 2
 
0.2%
-0.290247098 2
 
0.2%
0.3686865985 2
 
0.2%
Other values (1099) 1150
97.8%
ValueCountFrequency (%)
-3.476415259 1
0.1%
-3.347440048 1
0.1%
-3.320687667 1
0.1%
-3.213585445 1
0.1%
-2.767829016 1
0.1%
-2.620752021 1
0.1%
-2.611319585 1
0.1%
-2.578132981 1
0.1%
-2.542683654 1
0.1%
-2.516666763 1
0.1%
ValueCountFrequency (%)
3.389421795 1
0.1%
3.376599698 1
0.1%
3.358497915 1
0.1%
3.342658854 1
0.1%
3.333607962 1
0.1%
3.320785865 1
0.1%
3.280811092 1
0.1%
3.183514003 1
0.1%
3.175971593 1
0.1%
3.175217352 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
951 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%

Length

2023-05-21T15:40:14.061573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:14.159508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 951
80.9%
1 225
 
19.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1
828 
0
348 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%

Length

2023-05-21T15:40:14.237328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:14.331935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%

Most occurring characters

ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 828
70.4%
0 348
29.6%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
697 
1
479 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%

Length

2023-05-21T15:40:14.414342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:14.513045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%

Most occurring characters

ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 697
59.3%
1 479
40.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1042 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%

Length

2023-05-21T15:40:14.596858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:14.702563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1042
88.6%
1 134
 
11.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
812 
1
364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%

Length

2023-05-21T15:40:14.785367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:14.886137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%

Most occurring characters

ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 812
69.0%
1 364
31.0%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1114 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%

Length

2023-05-21T15:40:14.971888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:15.080616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1114
94.7%
1 62
 
5.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1061 
1
115 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Length

2023-05-21T15:40:15.166367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:15.279628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1061
90.2%
1 115
 
9.8%

Gender_Male
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1
704 
0
472 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%

Length

2023-05-21T15:40:15.404220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:15.527862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%

Most occurring characters

ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 704
59.9%
0 472
40.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1135 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%

Length

2023-05-21T15:40:15.630025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:15.743637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1135
96.5%
1 41
 
3.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
961 
1
215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

Length

2023-05-21T15:40:15.827190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:15.924455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 961
81.7%
1 215
 
18.3%

JobRole_Manager
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1093 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%

Length

2023-05-21T15:40:16.005112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:16.102104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1093
92.9%
1 83
 
7.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1063 
1
113 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

Length

2023-05-21T15:40:16.181984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:16.286140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1063
90.4%
1 113
 
9.6%

JobRole_Research Director
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1113 
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%

Length

2023-05-21T15:40:16.382877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:16.501568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1113
94.6%
1 63
 
5.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
956 
1
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%

Length

2023-05-21T15:40:16.581346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:16.676268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 956
81.3%
1 220
 
18.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
903 
1
273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%

Length

2023-05-21T15:40:16.756580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:16.851450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 903
76.8%
1 273
 
23.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1116 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%

Length

2023-05-21T15:40:17.025163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:17.118169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1116
94.9%
1 60
 
5.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
636 
1
540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%

Length

2023-05-21T15:40:17.201556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:17.300287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%

Most occurring characters

ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 636
54.1%
1 540
45.9%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
803 
1
373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

Length

2023-05-21T15:40:17.384079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:17.486001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

Most occurring characters

ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 803
68.3%
1 373
31.7%

OverTime_Yes
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
836 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Length

2023-05-21T15:40:17.567541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T15:40:17.664325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Most occurring characters

ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836
71.1%
1 340
28.9%

Interactions

2023-05-21T15:40:06.477219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:46.925791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.417064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:49.906924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.434608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.096711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:54.696617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.225515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.792075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.214563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.693520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.108171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.642552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.063935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.573961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.032463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.515849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:50.009958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.541337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.200507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:54.798139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.317250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.884123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.313301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.785275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.203175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.737043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.156975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.682635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.136058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.620048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:50.122467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.653024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.312240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:54.910031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.424850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.984857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.421016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.890985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.302949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.840408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.261389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.791184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.241513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.733005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:50.237649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.770148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.429332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:55.024498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.534179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.095973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.532640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.997708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.419901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.957096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.367793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:47.353114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:59.650349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.110406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.537587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.071511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.484851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.021395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.462875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.969233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:50.472306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:56.776531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.320077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.765876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.219557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:47.569649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:50.586537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.131879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.776485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:55.376082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.889253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.432774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.876581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.327537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.863635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.288749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.706783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.237666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.663362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:49.185225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:50.689993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.239991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:55.479955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.983975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.528971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.978914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.421340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:40:04.386483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.801542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.336367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:50.792209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.347159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:53.988359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:55.585431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.080716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.628882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:40:01.515030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:40:05.895228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:54.100012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:57.182170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:58.731607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.185190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.620631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.154936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.583941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:05.997260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.544405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:47.951513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:49.497549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.009475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.567267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-21T15:39:58.921231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.384446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.809359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.346943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.774448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.187784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.745065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.223783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:49.698466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.217437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.877528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:54.477726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.010041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.472981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.015981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.482201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:01.908465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.444419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.868197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.280698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:07.844375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:48.317631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:49.797188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:51.322943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:52.983016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:54.583725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:56.112797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:57.691399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:39:59.112521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:00.585638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:02.007237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:03.539815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:04.960916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T15:40:06.375295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-21T15:40:17.797929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeDailyRateDistanceFromHomeMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionRelationshipSatisfactionStockOptionLevelWorkLifeBalanceBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_MaleJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
Age1.0000.0160.0010.4820.3620.0080.670-0.0050.2530.2060.1730.1960.0310.0650.1430.0000.0500.3010.0000.0220.1010.0600.0460.0230.0000.0000.0000.0480.0000.0000.0430.1490.3170.0710.2080.1460.1090.2350.1040.2070.060
DailyRate0.0161.0000.0020.0380.0450.0170.029-0.0280.0050.029-0.0380.0010.0250.0220.0000.0000.0000.0000.0000.0000.0400.0000.0000.0360.0000.0730.0140.0000.0800.0540.0000.0000.0000.0000.0230.0000.0000.0000.1220.0720.000
DistanceFromHome0.0010.0021.0000.0100.0040.0420.009-0.0310.0130.015-0.0010.006-0.017-0.0490.0000.0000.0210.0510.0100.0380.0000.0000.0360.0720.0480.0000.0000.0000.0000.0440.0000.0000.0510.0300.0000.0000.0380.0570.0000.0000.057
MonthlyIncome0.4820.0380.0101.0000.176-0.0320.708-0.0090.4590.3870.2570.361-0.0270.3360.0830.0000.0480.8590.0000.0000.0630.0000.0700.0000.0000.1350.0000.0490.0440.0680.0790.3870.7250.2490.5290.3900.4700.2940.0260.0460.065
NumCompaniesWorked0.3620.0450.0040.1761.000-0.0220.317-0.029-0.170-0.117-0.068-0.138-0.0180.1040.0960.0000.0000.1130.0000.0000.0130.0350.0000.0360.0630.0610.0140.0610.0200.0000.0850.0610.1100.0000.1270.0400.0450.0950.0000.0780.018
PercentSalaryHike0.0080.0170.042-0.032-0.0221.000-0.0160.006-0.041-0.014-0.025-0.019-0.0130.0140.0350.0000.0570.0000.0000.0110.0000.0000.0560.0780.0100.0000.0000.0000.0340.0460.0000.0180.0610.0000.0030.0320.0530.0550.0000.0000.000
TotalWorkingYears0.6700.0290.0090.7080.317-0.0161.000-0.0040.5830.4820.3210.487-0.0350.1080.0840.0000.0050.5390.0380.0270.0740.0000.0180.0000.0000.0360.0490.0050.0180.0580.0780.2210.5620.0790.3150.2280.2110.3380.0830.1060.000
TrainingTimesLastYear-0.005-0.028-0.031-0.009-0.0290.006-0.0041.0000.0130.0110.005-0.003-0.0120.0000.0480.0240.0000.0000.0220.0000.0140.0000.0000.0000.0210.0720.0770.0000.0200.0000.0000.0000.0000.0590.0000.0470.0000.0550.0320.0200.111
YearsAtCompany0.2530.0050.0130.459-0.170-0.0410.5830.0131.0000.8460.5120.847-0.0090.0530.0790.0070.0360.3490.0000.0000.0180.0000.0000.0560.0340.0000.0410.0520.0000.1000.0000.1420.3900.0690.1610.1070.1340.2050.0670.0000.083
YearsInCurrentRole0.2060.0290.0150.387-0.117-0.0140.4820.0110.8461.0000.5050.724-0.0270.0640.0380.0610.0530.2410.0380.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.1290.2080.0390.1610.1370.1600.1380.0160.0610.000
YearsSinceLastPromotion0.173-0.038-0.0010.257-0.068-0.0250.3210.0050.5120.5051.0000.4610.0160.0530.0000.0000.0000.2140.0000.0290.0440.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0680.2410.0000.1130.1090.0690.0650.1000.0000.046
YearsWithCurrManager0.1960.0010.0060.361-0.138-0.0190.487-0.0030.8470.7240.4611.000-0.0410.0320.0550.0000.0260.2290.0000.0000.0000.0410.0960.1110.0000.0540.0000.0000.0000.0560.0000.1110.1950.0670.1730.1010.0790.1580.0000.0000.000
AverageSatisfaction0.0310.025-0.017-0.027-0.018-0.013-0.035-0.012-0.009-0.0270.016-0.0411.000-0.0370.0300.3680.0090.0540.3570.3610.0800.0000.0000.0600.0000.0000.0760.0000.0000.0560.0430.0550.0000.0000.0000.0000.0000.0000.0610.0980.107
SalaryDeviation0.0650.022-0.0490.3360.1040.0140.1080.0000.0530.0640.0530.032-0.0371.0000.0280.0420.0000.2930.0000.0500.0000.0000.0320.0490.0000.0360.0000.0000.0000.0280.0200.1560.2080.1520.3020.1820.1690.1180.0000.0430.000
Education0.1430.0000.0000.0830.0960.0350.0840.0480.0790.0380.0000.0550.0300.0281.0000.0490.0000.0820.0000.0320.0250.0000.0000.0000.0000.0500.0380.0710.0000.0300.0000.0480.0000.0000.0680.0000.0000.0620.0000.0000.000
EnvironmentSatisfaction0.0000.0000.0000.0000.0000.0000.0000.0240.0070.0610.0000.0000.3680.0420.0491.0000.0440.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0270.0000.0000.0380.0000.0000.0700.0000.0000.0160.0560.0610.0000.066
JobInvolvement0.0500.0000.0210.0480.0000.0570.0050.0000.0360.0530.0000.0260.0090.0000.0000.0441.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0430.0300.0240.000
JobLevel0.3010.0000.0510.8590.1130.0000.5390.0000.3490.2410.2140.2290.0540.2930.0820.0000.0001.0000.0000.0000.0630.0000.0000.0000.0000.1640.0050.0000.0700.0640.1210.4140.6490.2680.4310.4310.4810.2730.0410.0610.000
JobSatisfaction0.0000.0000.0100.0000.0000.0000.0380.0220.0000.0380.0000.0000.3570.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0460.0320.0000.0000.0000.0000.0290.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.000
RelationshipSatisfaction0.0220.0000.0380.0000.0000.0110.0270.0000.0000.0470.0290.0000.3610.0500.0320.0000.0000.0000.0001.0000.0260.0000.0310.0520.0000.0730.0330.0000.0580.0340.0000.0750.0000.0000.0000.0570.0470.0000.0420.0000.043
StockOptionLevel0.1010.0400.0000.0630.0130.0000.0740.0140.0180.0000.0440.0000.0800.0000.0250.0000.0400.0630.0000.0261.0000.0260.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0640.0000.0000.0000.0290.0270.3790.7870.000
WorkLifeBalance0.0600.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0261.0000.0000.0130.0660.0260.0330.0000.0150.0000.0290.0080.0000.0000.0000.0530.0220.0310.0000.0000.000
BusinessTravel_Travel_Frequently0.0460.0000.0360.0700.0000.0560.0180.0000.0000.0000.0130.0960.0000.0320.0000.0000.0000.0000.0000.0310.0000.0001.0000.7480.0430.0000.0000.0310.0270.0280.0000.0000.0450.0000.0000.0000.0000.0270.0170.0000.000
BusinessTravel_Travel_Rarely0.0230.0360.0720.0000.0360.0780.0000.0000.0560.0000.0000.1110.0600.0490.0000.0000.0000.0000.0460.0520.0000.0130.7481.0000.0340.0080.0000.0280.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0360.0000.000
EducationField_Life Sciences0.0000.0000.0480.0000.0630.0100.0000.0210.0340.0000.0000.0000.0000.0000.0000.0430.0000.0000.0320.0000.0000.0660.0430.0341.0000.2930.5530.1900.2690.0000.0510.0550.0000.0330.0230.0000.0970.0460.0150.0370.000
EducationField_Marketing0.0000.0730.0000.1350.0610.0000.0360.0720.0000.0000.0000.0540.0000.0360.0500.0000.0000.1640.0000.0730.0000.0260.0000.0080.2931.0000.2360.0730.1100.0110.0530.1640.0000.1090.0740.1660.4450.1510.0000.0000.000
EducationField_Medical0.0000.0140.0000.0000.0140.0000.0490.0770.0410.0000.0000.0000.0760.0000.0380.0000.0000.0050.0000.0330.0320.0330.0000.0000.5530.2361.0000.1510.2150.0000.0430.0370.0000.0000.0290.0390.1000.0420.0000.0000.000
EducationField_Other0.0480.0000.0000.0490.0610.0000.0050.0000.0520.0000.0000.0000.0000.0000.0710.0270.0000.0000.0000.0000.0000.0000.0310.0280.1900.0730.1511.0000.0650.0000.0000.0530.0000.0000.0000.0000.0440.0000.0000.0180.000
EducationField_Technical Degree0.0000.0800.0000.0440.0200.0340.0180.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0580.0000.0150.0270.0000.2690.1100.2150.0651.0000.0000.0000.0000.0280.0000.0000.0750.0550.0370.0000.0000.000
Gender_Male0.0000.0540.0440.0680.0000.0460.0580.0000.1000.1020.0000.0560.0560.0280.0300.0000.0000.0640.0290.0340.0000.0000.0280.0000.0000.0110.0000.0000.0001.0000.0230.0600.0200.0780.0000.0000.0000.0000.0000.0000.031
JobRole_Human Resources0.0430.0000.0000.0790.0850.0000.0780.0000.0000.0000.0000.0000.0430.0200.0000.0380.0000.1210.0000.0000.0000.0290.0000.0000.0510.0530.0430.0000.0000.0231.0000.0790.0320.0460.0190.0800.0950.0170.0180.0460.000
JobRole_Laboratory Technician0.1490.0000.0000.3870.0610.0180.2210.0000.1420.1290.0680.1110.0550.1560.0480.0000.0000.4140.0000.0750.0000.0080.0000.0000.0550.1640.0370.0530.0000.0600.0791.0000.1230.1480.1040.2220.2560.1010.0000.0000.000
JobRole_Manager0.3170.0000.0510.7250.1100.0610.5620.0000.3900.2080.2410.1950.0000.2080.0000.0000.0270.6490.0000.0000.0640.0000.0450.0000.0000.0000.0000.0000.0280.0200.0320.1231.0000.0790.0500.1250.1450.0480.0210.0180.000
JobRole_Manufacturing Director0.0710.0000.0300.2490.0000.0000.0790.0590.0690.0390.0000.0670.0000.1520.0000.0700.0000.2680.0510.0000.0000.0000.0000.0000.0330.1090.0000.0000.0000.0780.0460.1480.0791.0000.0650.1500.1730.0630.0000.0000.000
JobRole_Research Director0.2080.0230.0000.5290.1270.0030.3150.0000.1610.1610.1130.1730.0000.3020.0680.0000.0000.4310.0000.0000.0000.0000.0000.0180.0230.0740.0290.0000.0000.0000.0190.1040.0500.0651.0000.1050.1230.0360.0000.0000.000
JobRole_Research Scientist0.1460.0000.0000.3900.0400.0320.2280.0470.1070.1370.1090.1010.0000.1820.0000.0000.0000.4310.0000.0570.0000.0530.0000.0000.0000.1660.0390.0000.0750.0000.0800.2220.1250.1500.1051.0000.2600.1020.0410.0290.024
JobRole_Sales Executive0.1090.0000.0380.4700.0450.0530.2110.0000.1340.1600.0690.0790.0000.1690.0000.0160.0000.4810.0000.0470.0290.0220.0000.0000.0970.4450.1000.0440.0550.0000.0950.2560.1450.1730.1230.2601.0000.1190.0000.0000.000
JobRole_Sales Representative0.2350.0000.0570.2940.0950.0550.3380.0550.2050.1380.0650.1580.0000.1180.0620.0560.0430.2730.0000.0000.0270.0310.0270.0000.0460.1510.0420.0000.0370.0000.0170.1010.0480.0630.0360.1020.1191.0000.0000.0640.000
MaritalStatus_Married0.1040.1220.0000.0260.0000.0000.0830.0320.0670.0160.1000.0000.0610.0000.0000.0610.0300.0410.0000.0420.3790.0000.0170.0360.0150.0000.0000.0000.0000.0000.0180.0000.0210.0000.0000.0410.0000.0001.0000.6260.000
MaritalStatus_Single0.2070.0720.0000.0460.0780.0000.1060.0200.0000.0610.0000.0000.0980.0430.0000.0000.0240.0610.0000.0000.7870.0000.0000.0000.0370.0000.0000.0180.0000.0000.0460.0000.0180.0000.0000.0290.0000.0640.6261.0000.000
OverTime_Yes0.0600.0000.0570.0650.0180.0000.0000.1110.0830.0000.0460.0000.1070.0000.0000.0660.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0240.0000.0000.0000.0001.000

Missing values

2023-05-21T15:40:08.162588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-21T15:40:08.797855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_MaleJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
01.0901941.049455-0.8999151.064209-0.6587101.7952821.762189-0.6479972.0267521.330763-0.3371290.2402182.6131002.261482-0.6053890.337621-0.665706-0.625365-0.368024-0.616406-0.6215520.3517120110000000100000000
1-1.634828-0.523449-0.899915-1.8553320.2602020.373564-0.9862651.153526-0.864408-1.083704-0.3371290.2402180.247430-1.072675-0.6053890.337621-0.830071-0.905635-0.056884-0.8970470.965160-0.2004920100001101000000100
20.981193-0.992080-0.777610-1.855332-1.5776220.3735641.7621890.2527652.3477060.123529-0.8809741.1604030.2474301.4920610.1909620.3376210.8135781.3365270.5653981.348076-0.0926481.4777940101000100100000100
3-1.307825-0.4536530.445433-1.855332-0.6587100.373564-0.9862650.252765-0.956202-0.681293-1.1528960.240218-0.935405-0.559727-1.4017400.337621-0.008246-0.064824-0.6791650.506155-0.092648-0.5225530100100100000001100
40.6541910.491086-0.0437842.0373901.1791140.373564-0.0701140.252765-0.1859560.123529-0.609051-0.6799660.247430-0.1750170.1909620.3376210.1561190.7759860.5653980.7867950.4362560.1318551000100101000000001
5-0.9808220.879950-0.8999151.064209-1.577622-2.469873-0.9862651.153526-0.6621200.928352-0.6090511.160403-0.935405-0.8162010.987313-1.055510-0.665706-0.625365-0.056884-0.6164060.4362560.5092480100100100000100101
61.5261970.072310-1.0222190.0910290.2602020.373564-0.9862651.153526-0.821414-0.681293-1.152896-0.679966-0.935405-1.329148-2.198092-1.055510-0.994436-1.185905-0.679165-1.1776870.436256-0.0496440110000100000100100
70.6541910.6929962.1576940.0910290.2602020.3735640.846038-1.5487580.9192162.135585-1.1528960.2402180.2474300.850877-1.4017400.3376210.1561190.775986-0.679165-0.897047-0.6215520.7548771010000000010000100
80.327188-1.7099820.567738-0.8821521.1791140.373564-0.070114-1.548758-0.409527-0.681293-0.0652060.2402180.247430-0.816201-0.6053890.337621-0.336976-0.064824-0.368024-0.616406-0.092648-0.6525560110000000000010100
9-1.089823-1.4931160.0785200.0910291.179114-1.048155-0.0701141.153526-0.166609-0.681293-1.1528961.160403-0.935405-0.6879640.1909620.337621-0.172611-0.6253650.565398-0.0551262.0229670.1997370101000000000010101
AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_MaleJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
1166-0.3268171.585389-0.288393-0.882152-0.6587100.373564-0.9862650.252765-0.910627-1.0837042.654020-0.6799660.2474300.0814570.9873130.3376210.6492131.6167970.8765380.786795-0.621552-0.3626540100001100000001101
11672.507205-0.952197-0.2883930.091029-1.5776220.3735642.678340-1.5487582.7993620.928352-1.1528961.160403-0.9354052.7744291.783664-2.4486413.6077821.0562572.7433821.628716-1.1504560.2807810110000000100000100
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